This study will test the predictive performance of two risk adjustment methods, one case-mix method, and a demographic model based on the AAPCC methodology using utilization and expenditure data from two pediatric populations. The results of this study will fill a gap in existing knowledge, since previous research on risk adjusters has focused primarily on elderly and employed populations. Claims data for children ages l8 and under from the Maryland Medicaid program and a private, non-profit HMO in Minneapolis will serve as the study populations for this research project. Over the past decade, risk assessment and risk adjustment strategies have received increased attention. This interest has accompanied a growing concern over the need to control selection bias in the health care marketplace. Through prospective risk adjustment models, insurers and health plans can potentially use current information to predict future use of health care services for enrolled populations. Risk adjusters also have been identified as valuable tools for activities such as quality assurance and utilization review. Under a health care reform model based on managed competition, risk adjusters would be a critical factor in equalizing risk across health plans, thereby creating a "level playing field" among competing plans and providers. From a pediatric standpoint, the lack of research on applicable risk adjusters is a serious problem and one that could severely affect children with special health care needs and the providers that serve these populations. Most likely, risk adjustment for children will be achieved by adapting models that have been developed for the elderly or the general population. However, the comparative performance of these models has never been tested on pediatric populations. The objective of this study is to fill that crucial gap. The analytic plan for this research project will encompass two stages of analysis. The first stage will involve multivariate regression at an individual-level to estimate the amount of variance in health expenditures explained by each of the models. For the second stage of analysis, the predictive performance of each model will be assessed on a group-level using a ratio of predicted over actual costs. The groups will include randomly selected groups of varying size and intentionally skewed groups. These skewed groups will represent patients expected to be high or low risk for future expenditures. The purpose of selecting skewed groups is to test whether the risk adjusters are able to minimize the effects of selection bias. In addition to testing the performance of current risk adjustment systems, this study will also examine criteria for risk assessment and risk adjustment in pediatric populations and will identify areas for future research.